Already mentioned are recent crowdsourcing efforts [1]}, [2]}. More related are PDTB [3]} and BECauSE [4]}, but where our resource goal is a much larger corpus, for the purpose of training a neural text generation model. Most related would be the extractive approach of DisSent [5]}, but where we focus specifically on causality, and derive a much larger corpus. [6]} tagged a small corpus of event pairs conjoined with “and” as causal or not causal. CaTeRS [7]} included causal relations from a commonsense reasoning standpoint. Richer Event Description [8]} integrates real-world temporal and causal relations between events into a unified framework. Table REF  contrasts the size of causal portion of prior resources with our own.
